Overview

Dataset statistics

Number of variables24
Number of observations327
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory60.2 KiB
Average record size in memory188.4 B

Variable types

Numeric12
Categorical12

Alerts

backing_instruments is highly overall correlated with style_RockHigh correlation
danceability is highly overall correlated with happinessHigh correlation
energy is highly overall correlated with happiness and 2 other fieldsHigh correlation
happiness is highly overall correlated with danceability and 1 other fieldsHigh correlation
loudness is highly overall correlated with energyHigh correlation
main_singers is highly overall correlated with gender_MixHigh correlation
speechiness is highly overall correlated with energyHigh correlation
style_Rock is highly overall correlated with backing_instrumentsHigh correlation
gender_Mix is highly overall correlated with main_singersHigh correlation
favourite_10 is highly imbalanced (75.9%)Imbalance
host_10 is highly imbalanced (75.9%)Imbalance
style_Opera is highly imbalanced (92.5%)Imbalance
style_Rock is highly imbalanced (62.2%)Imbalance
style_Traditional is highly imbalanced (60.0%)Imbalance
gender_Mix is highly imbalanced (60.0%)Imbalance
backing_dancers has 215 (65.7%) zerosZeros
backing_instruments has 232 (70.9%) zerosZeros
backing_singers has 206 (63.0%) zerosZeros

Reproduction

Analysis started2023-05-08 09:25:32.972623
Analysis finished2023-05-08 09:25:48.843717
Duration15.87 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

BPM
Real number (ℝ)

Distinct87
Distinct (%)26.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.01223
Minimum66
Maximum187
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-05-08T10:25:48.916546image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum66
5-th percentile79
Q196.5
median120
Q3130
95-th percentile155.7
Maximum187
Range121
Interquartile range (IQR)33.5

Descriptive statistics

Standard deviation23.51334
Coefficient of variation (CV)0.20267983
Kurtosis-0.29234038
Mean116.01223
Median Absolute Deviation (MAD)16
Skewness0.1640516
Sum37936
Variance552.87715
MonotonicityNot monotonic
2023-05-08T10:25:49.309151image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128 24
 
7.3%
120 14
 
4.3%
130 12
 
3.7%
90 12
 
3.7%
95 9
 
2.8%
126 9
 
2.8%
124 8
 
2.4%
122 7
 
2.1%
108 7
 
2.1%
123 6
 
1.8%
Other values (77) 219
67.0%
ValueCountFrequency (%)
66 1
 
0.3%
69 1
 
0.3%
70 2
 
0.6%
72 2
 
0.6%
73 1
 
0.3%
74 1
 
0.3%
75 5
1.5%
76 2
 
0.6%
78 1
 
0.3%
79 2
 
0.6%
ValueCountFrequency (%)
187 1
 
0.3%
176 1
 
0.3%
174 1
 
0.3%
172 3
0.9%
170 3
0.9%
164 1
 
0.3%
163 1
 
0.3%
162 1
 
0.3%
160 2
0.6%
158 2
0.6%

backing_dancers
Real number (ℝ)

Distinct6
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.94495413
Minimum0
Maximum5
Zeros215
Zeros (%)65.7%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-05-08T10:25:49.402909image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5015423
Coefficient of variation (CV)1.5890108
Kurtosis0.29702173
Mean0.94495413
Median Absolute Deviation (MAD)0
Skewness1.3195695
Sum309
Variance2.2546294
MonotonicityNot monotonic
2023-05-08T10:25:49.479703image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 215
65.7%
4 30
 
9.2%
2 27
 
8.3%
3 24
 
7.3%
1 23
 
7.0%
5 8
 
2.4%
ValueCountFrequency (%)
0 215
65.7%
1 23
 
7.0%
2 27
 
8.3%
3 24
 
7.3%
4 30
 
9.2%
5 8
 
2.4%
ValueCountFrequency (%)
5 8
 
2.4%
4 30
 
9.2%
3 24
 
7.3%
2 27
 
8.3%
1 23
 
7.0%
0 215
65.7%

backing_instruments
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.81957187
Minimum0
Maximum5
Zeros232
Zeros (%)70.9%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-05-08T10:25:49.552197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5069926
Coefficient of variation (CV)1.8387559
Kurtosis1.4859982
Mean0.81957187
Median Absolute Deviation (MAD)0
Skewness1.7005035
Sum268
Variance2.2710268
MonotonicityNot monotonic
2023-05-08T10:25:49.625998image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 232
70.9%
1 27
 
8.3%
4 20
 
6.1%
3 17
 
5.2%
5 16
 
4.9%
2 15
 
4.6%
ValueCountFrequency (%)
0 232
70.9%
1 27
 
8.3%
2 15
 
4.6%
3 17
 
5.2%
4 20
 
6.1%
5 16
 
4.9%
ValueCountFrequency (%)
5 16
 
4.9%
4 20
 
6.1%
3 17
 
5.2%
2 15
 
4.6%
1 27
 
8.3%
0 232
70.9%

backing_singers
Real number (ℝ)

Distinct6
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0703364
Minimum0
Maximum5
Zeros206
Zeros (%)63.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-05-08T10:25:49.698804image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5873177
Coefficient of variation (CV)1.4830083
Kurtosis-0.15345562
Mean1.0703364
Median Absolute Deviation (MAD)0
Skewness1.1479732
Sum350
Variance2.5195775
MonotonicityNot monotonic
2023-05-08T10:25:49.775106image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 206
63.0%
4 31
 
9.5%
2 30
 
9.2%
3 29
 
8.9%
1 19
 
5.8%
5 12
 
3.7%
ValueCountFrequency (%)
0 206
63.0%
1 19
 
5.8%
2 30
 
9.2%
3 29
 
8.9%
4 31
 
9.5%
5 12
 
3.7%
ValueCountFrequency (%)
5 12
 
3.7%
4 31
 
9.5%
3 29
 
8.9%
2 30
 
9.2%
1 19
 
5.8%
0 206
63.0%

danceability
Real number (ℝ)

Distinct64
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.792049
Minimum17
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-05-08T10:25:49.873869image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile30
Q147
median56
Q366
95-th percentile79.7
Maximum92
Range75
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.770832
Coefficient of variation (CV)0.26474798
Kurtosis-0.3453625
Mean55.792049
Median Absolute Deviation (MAD)10
Skewness-0.244143
Sum18244
Variance218.17748
MonotonicityNot monotonic
2023-05-08T10:25:49.983548image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57 13
 
4.0%
52 12
 
3.7%
58 11
 
3.4%
56 11
 
3.4%
66 11
 
3.4%
50 10
 
3.1%
53 10
 
3.1%
63 10
 
3.1%
65 9
 
2.8%
61 9
 
2.8%
Other values (54) 221
67.6%
ValueCountFrequency (%)
17 2
0.6%
20 1
 
0.3%
21 1
 
0.3%
22 2
0.6%
23 1
 
0.3%
25 1
 
0.3%
26 1
 
0.3%
28 4
1.2%
29 3
0.9%
30 2
0.6%
ValueCountFrequency (%)
92 1
 
0.3%
89 1
 
0.3%
83 6
1.8%
82 1
 
0.3%
81 6
1.8%
80 2
 
0.6%
79 1
 
0.3%
78 3
 
0.9%
76 4
1.2%
75 8
2.4%

energy
Real number (ℝ)

Distinct76
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.446483
Minimum9
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-05-08T10:25:50.106247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile31
Q155.5
median70
Q383
95-th percentile93
Maximum97
Range88
Interquartile range (IQR)27.5

Descriptive statistics

Standard deviation19.465308
Coefficient of variation (CV)0.28860374
Kurtosis-0.12624484
Mean67.446483
Median Absolute Deviation (MAD)13
Skewness-0.7236349
Sum22055
Variance378.8982
MonotonicityNot monotonic
2023-05-08T10:25:50.217950image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 12
 
3.7%
87 11
 
3.4%
67 11
 
3.4%
86 10
 
3.1%
82 9
 
2.8%
66 8
 
2.4%
79 8
 
2.4%
72 8
 
2.4%
74 7
 
2.1%
83 7
 
2.1%
Other values (66) 236
72.2%
ValueCountFrequency (%)
9 1
0.3%
10 1
0.3%
15 1
0.3%
18 2
0.6%
19 1
0.3%
20 1
0.3%
21 1
0.3%
24 1
0.3%
28 2
0.6%
29 2
0.6%
ValueCountFrequency (%)
97 2
 
0.6%
96 4
1.2%
95 2
 
0.6%
94 6
1.8%
93 4
1.2%
92 6
1.8%
91 6
1.8%
90 5
1.5%
89 6
1.8%
88 6
1.8%

favourite_10
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
0
314 
1
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters327
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 314
96.0%
1 13
 
4.0%

Length

2023-05-08T10:25:50.312696image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-08T10:25:50.405957image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 314
96.0%
1 13
 
4.0%

Most occurring characters

ValueCountFrequency (%)
0 314
96.0%
1 13
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 327
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 314
96.0%
1 13
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Common 327
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 314
96.0%
1 13
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 327
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 314
96.0%
1 13
 
4.0%

happiness
Real number (ℝ)

Distinct86
Distinct (%)26.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.360856
Minimum4
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-05-08T10:25:50.498709image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile15
Q128
median40
Q361.5
95-th percentile84.7
Maximum97
Range93
Interquartile range (IQR)33.5

Descriptive statistics

Standard deviation21.830034
Coefficient of variation (CV)0.49210127
Kurtosis-0.70648404
Mean44.360856
Median Absolute Deviation (MAD)15
Skewness0.45398068
Sum14506
Variance476.55036
MonotonicityNot monotonic
2023-05-08T10:25:50.613432image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 10
 
3.1%
42 8
 
2.4%
35 8
 
2.4%
52 8
 
2.4%
28 8
 
2.4%
38 7
 
2.1%
39 7
 
2.1%
36 7
 
2.1%
21 7
 
2.1%
25 7
 
2.1%
Other values (76) 250
76.5%
ValueCountFrequency (%)
4 2
 
0.6%
8 1
 
0.3%
9 3
0.9%
10 2
 
0.6%
11 1
 
0.3%
12 3
0.9%
13 1
 
0.3%
14 2
 
0.6%
15 5
1.5%
16 5
1.5%
ValueCountFrequency (%)
97 1
 
0.3%
96 3
0.9%
92 2
0.6%
89 1
 
0.3%
88 2
0.6%
87 4
1.2%
86 2
0.6%
85 2
0.6%
84 1
 
0.3%
83 1
 
0.3%

host_10
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
0
314 
1
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters327
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 314
96.0%
1 13
 
4.0%

Length

2023-05-08T10:25:50.711196image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-08T10:25:50.799417image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 314
96.0%
1 13
 
4.0%

Most occurring characters

ValueCountFrequency (%)
0 314
96.0%
1 13
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 327
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 314
96.0%
1 13
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Common 327
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 314
96.0%
1 13
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 327
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 314
96.0%
1 13
 
4.0%

instrument_10
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
0
283 
1
44 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters327
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 283
86.5%
1 44
 
13.5%

Length

2023-05-08T10:25:50.870376image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-08T10:25:50.958517image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 283
86.5%
1 44
 
13.5%

Most occurring characters

ValueCountFrequency (%)
0 283
86.5%
1 44
 
13.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 327
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 283
86.5%
1 44
 
13.5%

Most occurring scripts

ValueCountFrequency (%)
Common 327
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 283
86.5%
1 44
 
13.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 327
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 283
86.5%
1 44
 
13.5%

key
Real number (ℝ)

Distinct24
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean313.72
Minimum130.81
Maximum739.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-05-08T10:25:51.032942image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum130.81
5-th percentile146.83
Q1207.65
median261.63
Q3392
95-th percentile622.25
Maximum739.99
Range609.18
Interquartile range (IQR)184.35

Descriptive statistics

Standard deviation147.20773
Coefficient of variation (CV)0.46923285
Kurtosis1.0655319
Mean313.72
Median Absolute Deviation (MAD)87.6
Skewness1.1655288
Sum102586.44
Variance21670.115
MonotonicityNot monotonic
2023-05-08T10:25:51.115721image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
261.63 27
 
8.3%
246.94 22
 
6.7%
164.81 20
 
6.1%
233.08 17
 
5.2%
392 17
 
5.2%
174.61 16
 
4.9%
146.83 15
 
4.6%
220 15
 
4.6%
554.37 15
 
4.6%
440 14
 
4.3%
Other values (14) 149
45.6%
ValueCountFrequency (%)
130.81 14
4.3%
146.83 15
4.6%
164.81 20
6.1%
174.61 16
4.9%
196 13
4.0%
207.65 8
 
2.4%
220 15
4.6%
233.08 17
5.2%
246.94 22
6.7%
261.63 27
8.3%
ValueCountFrequency (%)
739.99 14
4.3%
622.25 4
 
1.2%
554.37 15
4.6%
493.88 6
 
1.8%
466.16 10
3.1%
440 14
4.3%
415.3 11
3.4%
392 17
5.2%
369.99 13
4.0%
349.23 13
4.0%

language
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
0
225 
1
102 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters327
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 225
68.8%
1 102
31.2%

Length

2023-05-08T10:25:51.203516image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-08T10:25:51.291281image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 225
68.8%
1 102
31.2%

Most occurring characters

ValueCountFrequency (%)
0 225
68.8%
1 102
31.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 327
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 225
68.8%
1 102
31.2%

Most occurring scripts

ValueCountFrequency (%)
Common 327
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 225
68.8%
1 102
31.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 327
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 225
68.8%
1 102
31.2%

liveness
Real number (ℝ)

Distinct52
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.070336
Minimum3
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-05-08T10:25:51.383033image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6
Q110
median13
Q323
95-th percentile40.7
Maximum90
Range87
Interquartile range (IQR)13

Descriptive statistics

Standard deviation12.943646
Coefficient of variation (CV)0.7162925
Kurtosis5.8516127
Mean18.070336
Median Absolute Deviation (MAD)5
Skewness2.041501
Sum5909
Variance167.53798
MonotonicityNot monotonic
2023-05-08T10:25:51.492740image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 35
 
10.7%
11 27
 
8.3%
7 21
 
6.4%
14 17
 
5.2%
9 16
 
4.9%
8 16
 
4.9%
13 15
 
4.6%
12 14
 
4.3%
6 13
 
4.0%
17 12
 
3.7%
Other values (42) 141
43.1%
ValueCountFrequency (%)
3 2
 
0.6%
4 1
 
0.3%
5 6
 
1.8%
6 13
 
4.0%
7 21
6.4%
8 16
4.9%
9 16
4.9%
10 35
10.7%
11 27
8.3%
12 14
 
4.3%
ValueCountFrequency (%)
90 1
0.3%
84 1
0.3%
73 1
0.3%
64 1
0.3%
59 1
0.3%
58 2
0.6%
53 2
0.6%
52 1
0.3%
49 1
0.3%
48 1
0.3%

loudness
Real number (ℝ)

Distinct15
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.0672783
Minimum-18
Maximum-2
Zeros0
Zeros (%)0.0%
Negative327
Negative (%)100.0%
Memory size2.7 KiB
2023-05-08T10:25:51.581503image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-18
5-th percentile-10
Q1-7
median-5
Q3-4
95-th percentile-3.3
Maximum-2
Range16
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.267752
Coefficient of variation (CV)-0.37376759
Kurtosis2.9056755
Mean-6.0672783
Median Absolute Deviation (MAD)1
Skewness-1.3294386
Sum-1984
Variance5.142699
MonotonicityNot monotonic
2023-05-08T10:25:51.659295image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
-5 81
24.8%
-4 67
20.5%
-6 51
15.6%
-7 35
10.7%
-8 34
10.4%
-10 17
 
5.2%
-3 14
 
4.3%
-9 13
 
4.0%
-11 4
 
1.2%
-12 3
 
0.9%
Other values (5) 8
 
2.4%
ValueCountFrequency (%)
-18 1
 
0.3%
-15 1
 
0.3%
-14 1
 
0.3%
-13 2
 
0.6%
-12 3
 
0.9%
-11 4
 
1.2%
-10 17
5.2%
-9 13
 
4.0%
-8 34
10.4%
-7 35
10.7%
ValueCountFrequency (%)
-2 3
 
0.9%
-3 14
 
4.3%
-4 67
20.5%
-5 81
24.8%
-6 51
15.6%
-7 35
10.7%
-8 34
10.4%
-9 13
 
4.0%
-10 17
 
5.2%
-11 4
 
1.2%

main_singers
Real number (ℝ)

Distinct6
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2568807
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-05-08T10:25:51.744641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.75213809
Coefficient of variation (CV)0.59841644
Kurtosis18.587263
Mean1.2568807
Median Absolute Deviation (MAD)0
Skewness4.0209185
Sum411
Variance0.56571171
MonotonicityNot monotonic
2023-05-08T10:25:51.822460image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 276
84.4%
2 35
 
10.7%
3 7
 
2.1%
4 4
 
1.2%
6 3
 
0.9%
5 2
 
0.6%
ValueCountFrequency (%)
1 276
84.4%
2 35
 
10.7%
3 7
 
2.1%
4 4
 
1.2%
5 2
 
0.6%
6 3
 
0.9%
ValueCountFrequency (%)
6 3
 
0.9%
5 2
 
0.6%
4 4
 
1.2%
3 7
 
2.1%
2 35
 
10.7%
1 276
84.4%

speechiness
Real number (ℝ)

Distinct21
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5749235
Minimum2
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-05-08T10:25:51.909736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q13
median4
Q36
95-th percentile12.7
Maximum42
Range40
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.1282783
Coefficient of variation (CV)0.74050851
Kurtosis26.187565
Mean5.5749235
Median Absolute Deviation (MAD)1
Skewness4.2033188
Sum1823
Variance17.042682
MonotonicityNot monotonic
2023-05-08T10:25:51.998059image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
3 97
29.7%
4 82
25.1%
5 43
13.1%
6 30
 
9.2%
7 19
 
5.8%
8 17
 
5.2%
9 6
 
1.8%
11 6
 
1.8%
10 6
 
1.8%
13 4
 
1.2%
Other values (11) 17
 
5.2%
ValueCountFrequency (%)
2 1
 
0.3%
3 97
29.7%
4 82
25.1%
5 43
13.1%
6 30
 
9.2%
7 19
 
5.8%
8 17
 
5.2%
9 6
 
1.8%
10 6
 
1.8%
11 6
 
1.8%
ValueCountFrequency (%)
42 1
 
0.3%
33 1
 
0.3%
23 1
 
0.3%
22 1
 
0.3%
21 2
0.6%
19 1
 
0.3%
17 1
 
0.3%
15 2
0.6%
14 3
0.9%
13 4
1.2%

target_10
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
0
264 
1
63 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters327
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 264
80.7%
1 63
 
19.3%

Length

2023-05-08T10:25:52.093879image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-08T10:25:52.182809image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 264
80.7%
1 63
 
19.3%

Most occurring characters

ValueCountFrequency (%)
0 264
80.7%
1 63
 
19.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 327
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 264
80.7%
1 63
 
19.3%

Most occurring scripts

ValueCountFrequency (%)
Common 327
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 264
80.7%
1 63
 
19.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 327
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 264
80.7%
1 63
 
19.3%

style_Dance
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
0.0
289 
1.0
38 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters981
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 289
88.4%
1.0 38
 
11.6%

Length

2023-05-08T10:25:52.256610image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-08T10:25:52.343380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 289
88.4%
1.0 38
 
11.6%

Most occurring characters

ValueCountFrequency (%)
0 616
62.8%
. 327
33.3%
1 38
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 654
66.7%
Other Punctuation 327
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 616
94.2%
1 38
 
5.8%
Other Punctuation
ValueCountFrequency (%)
. 327
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 981
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 616
62.8%
. 327
33.3%
1 38
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 981
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 616
62.8%
. 327
33.3%
1 38
 
3.9%

style_Opera
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
0.0
324 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters981
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 324
99.1%
1.0 3
 
0.9%

Length

2023-05-08T10:25:52.416185image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-08T10:25:52.505975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 324
99.1%
1.0 3
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 651
66.4%
. 327
33.3%
1 3
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 654
66.7%
Other Punctuation 327
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 651
99.5%
1 3
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 327
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 981
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 651
66.4%
. 327
33.3%
1 3
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 981
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 651
66.4%
. 327
33.3%
1 3
 
0.3%

style_Pop
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
0.0
182 
1.0
145 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters981
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 182
55.7%
1.0 145
44.3%

Length

2023-05-08T10:25:52.576844image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-08T10:25:52.664592image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 182
55.7%
1.0 145
44.3%

Most occurring characters

ValueCountFrequency (%)
0 509
51.9%
. 327
33.3%
1 145
 
14.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 654
66.7%
Other Punctuation 327
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 509
77.8%
1 145
 
22.2%
Other Punctuation
ValueCountFrequency (%)
. 327
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 981
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 509
51.9%
. 327
33.3%
1 145
 
14.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 981
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 509
51.9%
. 327
33.3%
1 145
 
14.8%

style_Rock
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
0.0
303 
1.0
 
24

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters981
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 303
92.7%
1.0 24
 
7.3%

Length

2023-05-08T10:25:52.738387image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-08T10:25:52.826156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 303
92.7%
1.0 24
 
7.3%

Most occurring characters

ValueCountFrequency (%)
0 630
64.2%
. 327
33.3%
1 24
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 654
66.7%
Other Punctuation 327
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 630
96.3%
1 24
 
3.7%
Other Punctuation
ValueCountFrequency (%)
. 327
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 981
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 630
64.2%
. 327
33.3%
1 24
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 981
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 630
64.2%
. 327
33.3%
1 24
 
2.4%
Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
0.0
301 
1.0
 
26

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters981
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 301
92.0%
1.0 26
 
8.0%

Length

2023-05-08T10:25:52.899959image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-08T10:25:52.987724image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 301
92.0%
1.0 26
 
8.0%

Most occurring characters

ValueCountFrequency (%)
0 628
64.0%
. 327
33.3%
1 26
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 654
66.7%
Other Punctuation 327
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 628
96.0%
1 26
 
4.0%
Other Punctuation
ValueCountFrequency (%)
. 327
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 981
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 628
64.0%
. 327
33.3%
1 26
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 981
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 628
64.0%
. 327
33.3%
1 26
 
2.7%

gender_Male
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
0.0
176 
1.0
151 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters981
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 176
53.8%
1.0 151
46.2%

Length

2023-05-08T10:25:53.058015image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-08T10:25:53.147802image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 176
53.8%
1.0 151
46.2%

Most occurring characters

ValueCountFrequency (%)
0 503
51.3%
. 327
33.3%
1 151
 
15.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 654
66.7%
Other Punctuation 327
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 503
76.9%
1 151
 
23.1%
Other Punctuation
ValueCountFrequency (%)
. 327
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 981
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 503
51.3%
. 327
33.3%
1 151
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 981
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 503
51.3%
. 327
33.3%
1 151
 
15.4%

gender_Mix
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
0.0
301 
1.0
 
26

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters981
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 301
92.0%
1.0 26
 
8.0%

Length

2023-05-08T10:25:53.219583image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-08T10:25:53.311340image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 301
92.0%
1.0 26
 
8.0%

Most occurring characters

ValueCountFrequency (%)
0 628
64.0%
. 327
33.3%
1 26
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 654
66.7%
Other Punctuation 327
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 628
96.0%
1 26
 
4.0%
Other Punctuation
ValueCountFrequency (%)
. 327
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 981
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 628
64.0%
. 327
33.3%
1 26
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 981
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 628
64.0%
. 327
33.3%
1 26
 
2.7%

Interactions

2023-05-08T10:25:47.300905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:35.628404image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:36.919517image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:37.932231image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:38.967956image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:40.012120image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:41.077733image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:42.277660image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:43.276140image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:44.297831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:45.251349image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:46.316396image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:47.388640image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:35.720663image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:37.004676image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:38.017572image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:39.054290image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:40.103341image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:41.159491image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:42.366423image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:43.359941image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:44.377616image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:45.342314image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:46.399195image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:47.474411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:35.808429image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:37.087410image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:38.099387image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:39.138090image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:40.192824image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:41.237310image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:42.447235image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:43.441945image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:44.454410image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:45.429111image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:46.478982image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:47.560182image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:35.896194image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:37.172155image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:38.180172image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:39.221095image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:40.280181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:41.322115image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:42.530983image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:43.524263image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:44.534199image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:45.515880image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:46.559767image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:47.644988image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:35.979978image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:37.251943image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:38.270646image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:39.305928image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:40.368917image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:41.399907image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:42.611794image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:43.610540image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:44.611949image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:45.605666image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:46.637585image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:47.737732image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:36.070323image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:37.341705image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:38.363034image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:39.400980image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:40.464193image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:41.487876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:42.699532image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:43.698403image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:44.696950image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:45.699420image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:46.725857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:47.819489image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:36.147566image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:37.418469image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:38.444846image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:39.483759image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:40.544980image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:41.560086image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:42.774359image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:43.775198image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:44.771231image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:45.781228image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:46.801191image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:47.905141image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:36.229347image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:37.499874image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:38.528622image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:39.572048image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:40.628756image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:41.636881image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:42.851158image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:43.859972image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:44.846429image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:45.866971image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:46.881975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:47.990912image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:36.512521image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:37.580692image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:38.612427image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:39.658162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:40.717526image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:41.714673image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:42.931938image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:43.944745image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:44.924194image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:45.954765image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:46.961766image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:48.071810image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:36.614875image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:37.664163image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:38.692185image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:39.737699image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:40.798339image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:41.789474image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:43.012845image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:44.033508image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:44.996031image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:46.039541image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:47.037807image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:48.167583image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:36.723584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:37.758374image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:38.787943image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:39.835438image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:40.894685image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:41.877239image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:43.107624image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:44.126501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:45.086820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:46.133260image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:47.130300image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:48.254323image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:36.824315image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:37.839507image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:38.873209image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:39.918278image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:40.981958image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:41.954033image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:43.187406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:44.207043image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:45.163611image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:46.218657image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T10:25:47.211085image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-05-08T10:25:53.398641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
BPMbacking_dancersbacking_instrumentsbacking_singersdanceabilityenergyhappinesskeylivenessloudnessmain_singersspeechinessfavourite_10host_10instrument_10languagetarget_10style_Dancestyle_Operastyle_Popstyle_Rockstyle_Traditionalgender_Malegender_Mix
BPM1.0000.0670.0080.0040.0720.2040.0730.029-0.0090.1360.0800.1250.1160.0000.0850.0000.1010.2020.0000.0000.1000.0000.1060.000
backing_dancers0.0671.000-0.290-0.0660.3220.2310.290-0.0890.0130.148-0.1460.2670.0940.0600.1280.0740.0000.2280.0000.0720.1350.2440.0000.019
backing_instruments0.008-0.2901.000-0.142-0.0260.116-0.030-0.074-0.0010.062-0.026-0.0340.0800.0000.1760.0000.1260.0720.0600.1380.5470.0350.1760.000
backing_singers0.004-0.066-0.1421.0000.0350.0890.0750.134-0.0930.106-0.035-0.0640.0000.2680.0450.0480.0000.1360.0000.1170.0380.0000.0920.146
danceability0.0720.322-0.0260.0351.0000.3600.592-0.059-0.1170.1180.0480.4050.0000.0000.0000.0490.0990.1620.2120.2630.0000.2480.0830.000
energy0.2040.2310.1160.0890.3601.0000.540-0.0830.0100.6600.1430.5110.0000.1270.0000.0000.0780.2700.1220.1540.1700.0000.0000.083
happiness0.0730.290-0.0300.0750.5920.5401.000-0.031-0.0200.3050.1050.4230.0000.0650.1370.0000.0000.2860.1580.2450.0000.1080.1160.124
key0.029-0.089-0.0740.134-0.059-0.083-0.0311.000-0.0820.0260.019-0.1270.0000.0000.0000.0860.0000.1070.0300.0000.0640.0000.0000.000
liveness-0.0090.013-0.001-0.093-0.1170.010-0.020-0.0821.000-0.036-0.0160.1280.0000.0000.1180.0000.0000.0000.0520.1300.0000.2030.1360.000
loudness0.1360.1480.0620.1060.1180.6600.3050.026-0.0361.0000.0390.2590.0850.0000.1430.0000.1410.0000.0000.1100.0000.0370.0190.000
main_singers0.080-0.146-0.026-0.0350.0480.1430.1050.019-0.0160.0391.0000.0270.0000.0000.1600.0750.0000.0000.0000.0000.0000.1410.1360.738
speechiness0.1250.267-0.034-0.0640.4050.5110.423-0.1270.1280.2590.0271.0000.0000.0000.0720.0520.0290.1570.0000.0960.1400.1470.0530.000
favourite_100.1160.0940.0800.0000.0000.0000.0000.0000.0000.0850.0000.0001.0000.0000.0000.0000.2720.0000.0000.0000.0000.0000.0000.000
host_100.0000.0600.0000.2680.0000.1270.0650.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0560.000
instrument_100.0850.1280.1760.0450.0000.0000.1370.0000.1180.1430.1600.0720.0000.0001.0000.0290.0000.0850.0000.0000.0980.0830.1170.156
language0.0000.0740.0000.0480.0490.0000.0000.0860.0000.0000.0750.0520.0000.0000.0291.0000.0000.0410.0000.1600.0000.2230.0250.000
target_100.1010.0000.1260.0000.0990.0780.0000.0000.0000.1410.0000.0290.2720.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.000
style_Dance0.2020.2280.0720.1360.1620.2700.2860.1070.0000.0000.0000.1570.0000.0000.0850.0410.0001.0000.0000.3100.0630.0700.0000.000
style_Opera0.0000.0000.0600.0000.2120.1220.1580.0300.0520.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.000
style_Pop0.0000.0720.1380.1170.2630.1540.2450.0000.1300.1100.0000.0960.0000.0000.0000.1600.0000.3100.0001.0000.2330.2450.0000.000
style_Rock0.1000.1350.5470.0380.0000.1700.0000.0640.0000.0000.0000.1400.0000.0000.0980.0000.0000.0630.0000.2331.0000.0260.1150.026
style_Traditional0.0000.2440.0350.0000.2480.0000.1080.0000.2030.0370.1410.1470.0000.0000.0830.2230.0000.0700.0000.2450.0261.0000.0000.023
gender_Male0.1060.0000.1760.0920.0830.0000.1160.0000.1360.0190.1360.0530.0000.0560.1170.0250.0000.0000.0000.0000.1150.0001.0000.255
gender_Mix0.0000.0190.0000.1460.0000.0830.1240.0000.0000.0000.7380.0000.0000.0000.1560.0000.0000.0000.0000.0000.0260.0230.2551.000

Missing values

2023-05-08T10:25:48.415890image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-08T10:25:48.724067image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

BPMbacking_dancersbacking_instrumentsbacking_singersdanceabilityenergyfavourite_10happinesshost_10instrument_10keylanguagelivenessloudnessmain_singersspeechinesstarget_10style_Dancestyle_Operastyle_Popstyle_Rockstyle_Traditionalgender_Malegender_Mix
093000685605000261.63131-81300.00.00.00.00.00.00.0
1116000421803900349.23011-111400.00.00.00.00.01.00.0
2105122838213200293.66112-51410.00.00.00.01.01.00.0
387000324801500146.83114-61300.00.00.00.00.00.00.0
4142050708809600261.63132-311400.00.00.00.01.01.00.0
583000643403800233.08111-116400.00.00.00.00.00.00.0
6100010585203501146.8317-123300.00.00.00.01.00.00.0
7150000483702000349.23013-81700.00.00.00.00.00.00.0
8124400745305500196.0009-102400.00.01.00.00.01.00.0
9170000566405301261.63045-81600.00.00.00.00.00.00.0
BPMbacking_dancersbacking_instrumentsbacking_singersdanceabilityenergyfavourite_10happinesshost_10instrument_10keylanguagelivenessloudnessmain_singersspeechinesstarget_10style_Dancestyle_Operastyle_Popstyle_Rockstyle_Traditionalgender_Malegender_Mix
317103013355804000220.0019-81400.00.00.00.00.01.00.0
31890401578605200369.99117-51500.00.00.00.01.00.00.0
319128302686706700207.6508-81501.00.00.00.00.00.00.0
320125500619208501369.99064-41600.00.01.00.00.00.00.0
321124032697206401174.61133-61400.00.00.00.00.00.00.0
32292000265802300440.00190-81800.00.00.00.00.00.00.0
323130005649404510146.83126-41500.00.00.00.00.00.00.0
324136311628905600233.08020-412100.00.01.00.00.01.00.0
325105050303101200329.63029-141310.00.00.00.00.00.00.0
326126203697008700174.61114-71301.00.00.00.00.00.00.0